code: def _pad_segment(input, segment_size):
      # input is the features: (B, N, T)
      batch_size, dim, seq_len = input.shape
      segment_stride = segment_size // 2
  
      rest = (
          segment_size
          - (segment_stride + seq_len % segment_size) % segment_size
      )
      if rest > 0:
          pad = Variable(torch.zeros(batch_size, dim, rest)).type(
              input.type()
          )
          input = torch.cat([input, pad], 2)
  
      pad_aux = Variable(
          torch.zeros(batch_size, dim, segment_stride)
      ).type(input.type())
      input = torch.cat([pad_aux, input, pad_aux], 2)
  
      return input, rest
  
file_path: espnet/espnet2/enh/layers/dprnn.py
imports: import torch
  import torch.nn
  from torch.autograd import Variable
label_desc: Use image node_id 1 for calling a global function with example usage: _pad_segment(input, segment_size) and returns: input,  rest